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Abstract Objective: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. Method: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. Results: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. Conclusions: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.
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Abstract Objective: Reliably prediction models for coronary artery abnormalities (CAA) in children aged > 5 years with Kawasaki disease (KD) are still lacking. This study aimed to develop a nomogram model for predicting CAA at 4 to 8 weeks of illness in children with KD older than 5 years. Methods: A total of 644 eligible children were randomly assigned to a training cohort (n = 450) and a validation cohort (n = 194). The least absolute shrinkage and selection operator (LASSO) analysis was used for optimal predictors selection, and multivariate logistic regression was used to develop a nomogram model based on the selected predictors. Area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, Brier score, and decision curve analysis (DCA) were used to assess model performance. Results: Neutrophil to lymphocyte ratio, intravenous immunoglobulin resistance, and maximum baseline z-score ≥ 2.5 were identified by LASSO as significant predictors. The model incorporating these variables showed good discrimination and calibration capacities in both training and validation cohorts. The AUC of the training cohort and validation cohort were 0.854 and 0.850, respectively. The DCA confirmed the clinical usefulness of the nomogram model. Conclusions: A novel nomogram model was established to accurately assess the risk of CAA at 4-8 weeks of onset among KD children older than 5 years, which may aid clinical decisionmaking.
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Abstract We aimed to develop a prognostic model for primary pontine hemorrhage (PPH) patients and validate the predictive value of the model for a good prognosis at 90 days. A total of 254 PPH patients were included for screening of the independent predictors of prognosis, and data were analyzed by univariate and multivariable logistic regression tests. The cases were then divided into training cohort (n=219) and validation cohort (n=35) based on the two centers. A nomogram was developed using independent predictors from the training cohort to predict the 90-day good outcome and was validated from the validation cohort. Glasgow Coma Scale score, normalized pixels (used to describe bleeding volume), and mechanical ventilation were significant predictors of a good outcome of PPH at 90 days in the training cohort (all P<0.05). The U test showed no statistical difference (P=0.892) between the training cohort and the validation cohort, suggesting the model fitted well. The new model showed good discrimination (area under the curve=0.833). The decision curve analysis of the nomogram of the training cohort indicated a great net benefit. The PPH nomogram comprising the Glasgow Coma Scale score, normalized pixels, and mechanical ventilation may facilitate predicting a 90-day good outcome.
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【Objective】 To explore the application value of 18F-PSMA PET/CT on the detection of metastatic lesions of prostate cancer with serum total prostate specific antigen (tPSA) ≤20 ng/mL and the predictive variables affecting the imaging results, and to establish a predictive nomogram for the metastasis of prostate cancer. 【Methods】 The imaging, pathological, serum and clinical data of 175 pathologically confirmed prostate cancer patients who underwent 18F-PSMA PET/CT examination during Jan.2020 and Oct.2021 were retrospectively collected.The patients were divided into metastatic group and non-metastatic group according to PET/CT imaging results, and the positive detection rate of metastatic lesions was calculated.The independent influencing factors of 18F-PSMA PET/CT in the positive detection of metastatic lesions were determined with univariate and multivariate logistic regression analyses.The predictive nomogram was established. 【Results】 Of the 175 patients, metastatic lesions were detected in 78 cases and not detected in 97 cases, with a detection rate of 44.6% (78/175).There were statistically significant differences between the metastatic group and the non-metastatic group in urinary tract symptoms, androgen deprivation treatment (ADT) at the time of PET/CT examination and the risk level of Gleason score (GS) (P<0.05).Univariate logistic regression showed that urinary tract symptoms(OR=3.64, P<0.001), GS risk (OR=3.96, P<0.001) and concurrent ADT treatment (OR=3.71, P<0.001) were associated with the positive detection rate of metastatic lesions.Multivariate Logistic regression showed that urinary tract symptoms (OR=3.19, P=0.002), GS high-risk group (OR=2.95, P=0.005) and concurrent ADT treatment (OR=3.27, P=0.001) were independent predictors of positive detection rate. 【Conclusion】 The probability of metastasis in newly diagnosed prostate cancer patients with tPSA≤20 ng/mL is high.18F-PSMA PET/CT is of high value for the early detection of metastasis.Urinary tract symptoms, GS high-risk group and concurrent ADT treatment are independent predictors of metastatic lesions.The predictive nomogram can help assist clinical optimization of imaging examination path.
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【Objective】 To establish a risk model for predicting spontaneous rupture bleeding of renal angiomyolipoma (RAML) in order to better assess and deal with the risk. 【Methods】 The information of 436 RAML patients diagnosed during Jan.2018 and Dec.2022 was retrospectively analyzed.According to the inclusion and exclusion criteria, 216 patients were included and divided into the rupture bleeding group (n=35) and non-rupture bleeding group (n=181).The factors influencing spontaneous rupture bleeding were identified using univariate and multivariate analysis, and a nomogram was constructed accordingly with R language.The nomogram was evaluated using Calibration curve and area under the receiver operator characteristic curve (AUC). 【Results】 It was found that clinical manifestations, tumor diameter, tumor convexity, tumor blood supply, and tuberous sclerosis complex (TSC) were significantly correlated with rupture bleeding.The Calibration curve fitted well with the nomogram.The AUC was 0.956 (95%CI: 0.856-0.943), indicating that the nomogram had good statistical performance. 【Conclusion】 The model can effectively predict the risk of spontaneous rupture bleeding of renal angiomyolipoma.
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【Objective】 To construct a nomogram survival prediction model for patients with locally advanced renal cell carcinoma based on SEER database (n=7893), so as to provide reference for future prognosis study. 【Methods】 Case data were downloaded from the SEER database, and divided into the experimental group and validation group with a ratio of 7∶3 by simple randomization.The clinical information was analyzed, independent risk factors influencing prognosis were screened, and the overall survival (OS) and tumor-specific survival (CSS) were mapped.Model performance was evaluated using consistency index, area under the receiver operating characteristic curve (AUC), internal and external validation, and calibration curves. 【Results】 Patients’ age, tumor size, disease progression tpye, TNM stage, number of positive lymph nodes, marital status and pathological type were significantly correlated with OS and CSS (P<0.01).Based on the above predictors, the internal verification AUC of the 1-, 3- and 5- year OS nomogram model was 0.809, 0.721 and 0.715, respectively.The internal validation AUC of the nomogram model for 1-, 3- and 5- year CSS was 0.802, 0.745 and 0.735, respectively.The external validation AUC of the OS nomogram model was 0.792, 0.628 and 0.620 at 1, 3 and 5 years, respectively, and the external validation AUC of CSS was 0.943, 0.803 and 0.737 at 1.3 and 5 years, respectively, showing good model differentiation and accuracy. 【Conclusion】 The prediction performance of the nomogram model is good, and it can provide reference for individualized treatment.
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【Objective】 To analyze the independent influencing factors of repeated extracorporeal shock wave lithotripsy (ESWL) in the treatment of upper urinary calculi (UUC), based on which a nomogram model was established to predict the efficacy. 【Methods】 Clinical and imaging data of 203 patients receiving repeated ESWL during Jan.2020 and Dec.2022 were collected, including 117 cases in the successful group and 86 cases in the unsuccessful group.The patients’ age and sex, stone volume (SV), surface area (SA), skin-to-site distance (SSD), maximum CT value, mean stone density (MSD), and stone heterogeneity index (SHI) were compared between the two groups.The independent predictors were analyzed with logistic regression and the meaningful variables (P<0.05) were used to establish a nomogram.The efficacy of the model was evaluated using receiver operating characteristic (ROC) curve and decreasing curve analysis (DCA).Internal validation was also performed. 【Results】 Stepwise regression showed that SV, SSD, MSD and SHI were independent influencing factors (P<0.05).The area under the ROC curve (AUC), optimal threshold, sensitivity and specificity were 0.793 (95%CI: 0.674-0.911), 0.619, 77.1% and 74.0%, respectively.The DCA curve was above two extreme curves.Hosmer-Lemeshow test and calibration curve showed that the nomogram had a good fitting degree (χ2=5.526, P=0.489), and the correction C-index was 0.746. 【Conclusion】 SV, SSD, MSD and SHI are independent predictors of the efficacy of repeated ESWL in the treatment of UUC.The nomogram established based on the above indicators has good predictive efficiency and clinical applicability.
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【Objective】 To identify the risk factors of patients of bone metastatic prostate cancer with high tumor load progressed to castration resistant prostate cancer (CRPC), establish a nomogram prediction model and evaluate its consistency and accuracy. 【Methods】 A total of 164 patients diagnosed by puncture and imaging during 2012 and 2022 were included.The general characteristics were analyzed with IBM SPSS software; the variables were screened with Cox regression; the multivariate risk factors with P<0.05 were included in the nomogram prediction model.The consistency and prediction accuracy of the model were evaluated with C-index, receiver operating characteristic (ROC) curve and calibration chart. 【Results】 In univariate analysis, initial prostate-specific antigen (PSA), prostate-specific antigen density (PSAD), Gleason score, T stage, alkaline phosphatase (ALP) and lactate dehydrogenase (LDH) were correlated with CRPC (P<0.05).Multivariate analysis showed that initial PSA, Gleason score, T stage, ALP and LDH were independent risk factors of CRPC (P<0.05).Based on the above five risk factors, a nomogram prediction model was constructed.The C-index was 0.801, the area under ROC curve (AUC) of 1-year progression-free survival (PFS) was 0.701 (0.608-0.794), and the AUC of 2-year PFS was 0.857 (0.767-0.947).The calibration chart showed that the prediction probability of the model was in good agreement with the actual probability. 【Conclusion】 Initial PSA, Gleason score, T stage, ALP and LDH are independent risk factors of CRPC.The predictive model may be an effective tool for the initial diagnosis of high tumor load bone metastatic prostate cancer, but more data are needed for internal and external validation.
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【Objective】 To investigate the prognostic value of tumor location in patients with upper tract urothelial carcinoma (UTUC) treated with radical nephroureterectomy (RNU), and to develop and validate a nomogram model for predicting the overall survival (OS). 【Methods】 UTUC patients undergoing RUN at our hospital during Jan.2010 and Dec.2022 were retrospectively collected, 70% of whom were included in the training group and 30% in the validation group.According to the tumor location, patients were divided into renal pelvis tumor (RPT) group and ureteral tumor (UT) group.The differences in clinicopathological features and prognosis were analyzed.Based on multivariate Cox results, a nomogram model for predicting OS was developed and validated. 【Results】 A total of 366 patients (196 RPT and 170 UT) were included in this study.There were statistically significantly differences in urine cytology (P=0.001), hydronephrosis (P<0.001), history of bladder tumor (P=0.021), pathological T stage (P<0.001) and histological structure (P=0.037) between the two groups.Multivariate Cox results showed that patients with UT had a worse prognosis (HR=2.00, 95%CI: 1.22-3.27, P=0.006).Factors of the nomogram for predicting OS included age, tumor location, lymphovascular invasion and pathological T stage.The model showed good discrimination and calibration, and performed well in internal verification. 【Conclusion】 Compared with RPT, UT has a worse prognosis and the fat around the tumor should be surgically removed more thoroughly to avoid micro-residual.We successfully coustructed a nomogram model that can be used to predict the OS of UTUC patients after RNU surgery.
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@#Objective To investigate the prognostic value of preoperative serum albumin-to-globulin ratio (AGR) and neutrophil-lymphocyte ratio (NLR) in the overall survival (OS) of patients with esophageal squamous cell carcinoma (ESCC), and to establish an individualized nomogram model and evaluate its efficacy, in order to provide a possible evaluation basis for the clinical treatment and postoperative follow-up of ESCC patients. Methods AGR, NLR, clinicopathological and follow-up data of ESCC patients diagnosed via pathology in the Department of Thoracic Surgery, The First Affiliated Hospital of Xinjiang Medical University from 2010 to 2017 were collected. The correlation between NLR/AGR and clinicopathological data were analyzed. Kaplan-Meier analysis and log-rank test were used for survival analysis. The optimal cut-off values of AGR and NLR were determined by X-tile software, and the patients were accordingly divided into a high-level group and a low-level group. At the same time, univariate and multivariate Cox regression analyses were used to identify independent risk factors affecting OS in the ESCC patients, and a nomogram prediction model was constructed and internally verified. The diagnostic efficacy of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve, and the clinical application value was evaluated by decision curve analysis. Results A total of 150 patients were included in this study, including 105 males and 45 females with a mean age of 62.3±9.3 years, and the follow-up time was 1-5 years. The 5-year OS rate of patients in the high-level AGR group was significantly higher than that in the low-level group (χ2=6.339, P=0.012), and the median OS of the two groups was 25 months and 12.5 months, respectively. The 5-year OS rate of patients in the high-level NLR group was significantly lower than that in the low-level NLR group (χ2=5.603, P=0.018), and the median OS of the two groups was 18 months and 39 months, respectively. Multivariate Cox analysis showed that AGR, NLR, T stage, lymph node metastasis, N stage, and differentiation were independent risk factors for the OS of ESCC patients. The C-index of the nomogram model was 0.689 [95%CI (0.640, 0.740)] after internal validation. The area under the ROC curve of predicting 1-, 3-, and 5-year OS rate was 0.773, 0.724 and 0.725, respectively. At the same time, the calibration curve and the decision curve suggest that the model had certain efficacy in predicting survival and prognosis. Conclusion Preoperative AGR and NLR are independent risk factors for ESCC patients. High level of AGR and low level of NLR may be associated with longer OS in the patients; the nomogram model based on AGR, NLR and clinicopathological features may be used as a method to predict the survival and prognosis of ESCC patients, which is expected to provide a reference for the development of personalized treatment for patients.
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ObjectiveThis study aims to explore risk factors for the development of major adverse cardiovascular and cerebrovascular events （MACCEs） in middle-aged and elderly patients with type 2 diabetes mellitus complicated with stable angina pectoris （T2DM-SAP） based on real-world clinical data in traditional Chinese medicine （TCM）， so as to develop a COX proportional risk prediction model and visualize the predicted results using a nomogram. MethodBased on the clinical scientific research information sharing system， the medical records of 586 T2DM-SAP patients （45-94 years old） were collected from January 2012 to December 2019， including age， gender， course of disease， major medical history， laboratory examination， tongue image， pulse image， TCM syndrome， and major treatment drugs. MACCE outcome indicators of patients were obtained by telephone follow-up and re-hospitalization records. The data was divided into a training set and a validation set according to 7∶3. In the training set， COX univariate analysis was used to determine the risk factors for MACCE in T2DM-SAP patients， and then variables were screened by forward-backward stepwise regression method， so as to establish a MACCE risk prediction model and construct a nomogram. The predictive efficacy of the model was reflected by the C-index， receiver operating characteristic （ROC） curve， calibration map， and clinical decision curve. ResultThe history of cerebrovascular disease ［Hazard ratio （HR）=1.983， 95% confidence interval （CI，1.314-2.993）］， low-density lipoprotein （LDL-C/mmol·L-1）≥4.1［HR=2.683， 95%CI（1.461-4.925）］， dull red tongue ［HR=1.955， 95%CI（1.273-3.002）］， dull purple tongue ［HR=4.214， 95%CI（2.017-8.803）］， white thick coating ［HR=3.030， 95%CI（1.634-9.293）］， thin and weak pulse ［HR=2.233， 95%CI（1.283-3.888）］， and syndrome of wind-phlegm blocking collaterals ［HR=2.007， 95%CI（1.179-3.418）］ were found to be risk factors in middle-aged and elderly T2DM-SAP patients. Insulin ［HR=0.604， 95%CI（0.399-0.914）］， glycosidase inhibitor ［HR=0.627， 95%CI（0.409-0.962）］， and TCM treatment ［HR=0.328， 95%CI（0.214-0.503）］ were protective factors in middle-aged and elderly T2DM-SAP patients. The prediction model was constructed based on the above risk factors. The C-index of the model was 0.818 （95% CI 0.777 -0.859） in the training set and 0.814 （95% CI 0.773-0.855） in the validation set， and the change of C-index over time was plotted. The AUC of patients for 5， 10， 15 years in the training set was 0.71， 0.67， and 0.61. The AUC of patients for 5， 10， and 15 years in the validation set was 0.60， 0.68， and 0.63， respectively. The calibration map and clinical decision curves of 5， 10， 15 years were drawn in the training set and the validation set， respectively. The model was well calibrated and clinically effective. ConclusionThe history of cerebrovascular disease， LDL， dull red tongue， dull purple tongue， white thick coating， thin and weak pulse， and syndrome of wind-phlegm blocking collaterals are risk factors for MACCE in middle-aged and elderly T2DM-SAP patients， and insulin， glycosidase inhibitors， TCM treatment are protective factors for MACCE in middle-aged and elderly T2DM-SAP patients. A clinical prediction model is established accordingly. This model has good discrimination， calibration degree， and clinical effectiveness and provides a scientific basis for the prevention and treatment of MACCE in middle-aged and elderly T2DM-SAP patients.
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Objective To evaluate the risk of cardiac adverse events in patients with malignant tumors after chemotherapy by using a combination of acoustic cardiography and blood indices. Methods A total of 171 patients with malignant tumor who received chemotherapy were included. They were divided into cardiac adverse event group and non-cardiac adverse event group in accordance with whether cardiac adverse events occurred after chemotherapy. The general data, blood indices before chemotherapy, and acoustic cardiography-related indices in the early stage (1-3 cycles) of chemotherapy of the two groups were analyzed. The possible influencing factors were determined by binary logistic regression analysis, and the nomogram was drawn. The receiver operating characteristic (ROC) curve was used to evaluate the prediction ability of the nomogram. Results Cardiac adverse events occurred in 44 of 171 patients with malignant tumors after chemotherapy, and the incidence of cardiac adverse events was 25.73%. Binary logistic regression results showed that age, red blood cell distribution width (RDW) before chemotherapy, activated partial thromboplastin time (APTT), and electromechanical activation time (EMAT) at the early stage of chemotherapy were independent predictors of cardiac adverse events in chemotherapy patients. The area under the ROC curve of the nomogram was 0.768 (95%CI: 0.693-0.843, P<0.001). Conclusion A nomogram based on age, pre-chemotherapy RDW, APTT, and EMAT at the early stage of chemotherapy is useful for early assessment of the risk of cardiac adverse events in chemotherapy patients.
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Objective @# To evaluate the prognostic value of a radiomics model based on the peritumoral region of gli- oma．@*Methods @#138 patients with glioma were retrospectively analyzed ，medical imaging interaction toolkit ( MITK) software was used to obtain the magnetic resonance imaging (MRI) images of peritumoral area 5 mm，10 mm and 20 mm from the tumor edge and extract texture features．The texture features were screened the radiomics model was established and the radiomic score was calculated．A clinical prediction model and a combined predic- tion model along with Rad-score and clinical risk factors were established．The combined prediction model was dis- played as a nomogram，and the predictive performance of the model for survival in glioma patients was evaluated. @*Results @# In the validation set，the C-index value of the radiomics model based on the peritumoral region 10 mm a- way from the tumor edge based on T2 weighted image (T2WI) images was 0. 663 (95% CI = 0. 72－0. 78) ，resul- ting in the best prediction performance．On the training set and validation set，the C-index of the nomogram was 0. 770 and 0. 730，respectively，indicating that the prediction performance of nomogram was better than those of the radiomics model and clinical prediction model．The model had the highest prediction effect on the 3-year survival rate of glioma patients (training set area under curve (AUC) = 0. 93，95% CI = 0. 83 － 0. 98 ; validation set AUC = 0. 88，95% CI = 0. 76 －0. 99) ．The calibration curve showed that the joint prediction nomogram in both the training set and the validation set had good performance．@*Conclusion @# The combined prediction model based on the preoperative T2WI images in the peritumoral region 10 mm from the tumor edge and the clinicopathological risk factors can accurately predict the prognosis of glioma，providing the best effect of prediction on the 3-year survival rate of glioma.
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AIM: To analyze and screen influencing factors of diabetic patients complicated with retinopathy, and establish and validate prediction model of nomogram.METHODS: A total of 1 252 patients from the Diabetes Complications Early Warning Dataset of the National Population Health Data Archive(PHDA)between January 2013 to January 2021 were selected and randomly divided into a modeling group(n=941)and a validation group(n=311). Univariate analysis, LASSO regression and Logistic regression analysis were used to screen out the influencing factors of diabetic retinopathy, and a nomogram prediction model was established. The receiver operating characteristic curve, Hosmer-Lemeshow test and calibration curve were used to evaluate the model. The clinical benefit was evaluated by the decision curve analysis(DCA).RESULTS: Age, hypertension, nephropathy, systolic blood pressure(SBP), glycated hemoglobin(HbA1c), high-density lipoprotein cholesterol(HDL-C), and blood urea(BU)were the influencing factors of diabetic retinopathy. The area under the curve(AUC)of the modeling group was 0.792(95%CI: 0.763-0.821), and the AUC of the validation group was 0.769(95%CI: 0.716-0.822). The Hosmer-Lemeshow goodness of fit test and calibration curve suggested that the theoretical value of the model was in good agreement(modeling group: χ2=14.520, P=0.069; validation group: χ2=14.400, P=0.072). The DCA results showed that the threshold probabilities range was 0.09-0.89 for modeling group and 0.07-0.84 for the validation group, which suggested the clinical net benefit was higher.CONCLUSION: This study constructed a risk prediction model including age, hypertension, nephropathy, SBP, HbA1c, HDL-C, and BU. The model has a high discrimination and consistency, and can be used to predict the risk of diabetic retinopathy in patients with diabetes.
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Objective@#To investigate the proportion of high-risk population for cardiovascular diseases (CVD) among residents at ages of 35 to 79 in Nanjing City, and establish a prediction model of high-risk population for CVD.@*Methods@#Residents at ages of 35 to 79 years were selected from Nanjing City using a multi-stage stratified cluster random sampling method from 2020 to 2021. Participants' demographic information, characteristics of lifestyle and blood biochemical index were collected using questionnaire surveys, physical examination and laboratory testing. The high-risk population for CVD were determined according to the Chinese Guidelines for Cardiovascular Disease Risk Assessment and Management, and the Chinese Guidelines for the Prevention and Treatment of Adult Dyslipidemia (2016 Revision). Predictive factors for high-risk population for CVD were screened using a multivariable logistic regression model. A nomogram was established and verified with receiver operation characteristic (ROC) curve. Hosmer-Lemeshow goodness of fit test was used to evaluate the fitting effect and Bootstrap method was used for internal verification.@*Results@#A total of 38 428 individuals were surveyed, including 17 970 males (46.76%) and 20 458 females (53.24%), and 25 714 individuals aged 35 to 59 years. There were 8 905 high-risk population for CVD, with a detection rate of 23.17%. Multivariable logistic regression analysis identified 9 factors affecting high-risk population for CVD. A prediction model was established for ln[P/(1-P)]=-7.305+2.107×age-0.366×gender+0.299×marital status-0.297×educational level+0.631×body mass index+0.013×sleep duration+0.096×edible salt intake+0.444×smoke-0.069×alcohol consumption. The area under ROC curve was 0.799 (95%CI: 0.794-0.805), the sensitivity and specificity were 0.731 and 0.753, indicating good differentiation. The nomogram based on the above factors indicated good calibration and stability.@*Conclusion@#The nomogram constructed by age, gender, marital status, educational level, body mass index, sleep duration, edible salt intake, smoking and alcohol consumption can be used to predict high-risk population for CVD.
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Objective@#To construct a prediction model for preeclampsia (PE) risk in twin-pregnant women, so as to provide the basis for early screening and prevention of PE.@*Methods@#A total of 467 twin-pregnant women who underwent prenatal examination and delivered at Huzhou Maternal and Child Health Hospital were selected. Sixty cases with preeclampsia (PE) were included in the case group, and 60 women without PE were included in the control group. General information, blood biochemical indicators and uterine artery resistance index (UtA-RI) were collected. A logistic regression model was used to screen predictive factors and establish a nomogram. The Bootstrap method was performed for the internal validation; the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis were employed to evaluate the discrimination, calibration and clinical utility of the nomogram, respectively.@*Results@#In the case group, there were 47 individuals (78.33%) aged younger than 35 years, 21 individuals (35.00%) with pre-pregnancy body mass index (BMI) of 25 kg/m2 and above, and 33 individuals (55.00%) with in vitro fertilization. In the control group, there were 57 individuals (95.00%) aged younger than 35 years, 8 individuals (13.33%) with pre-pregnancy BMI of 25 kg/m2 and above, and 39 individuals (65.00%) with natural pregnancy. Multivariable logistic regression analysis identified age, pre-pregnancy BMI, method of conception, placental growth factor (PLGF) and UtA-RI as risk prediction factors for PE in twin-pregnant women. The established nomogram had an area under the ROC curve of 0.827 (95%CI: 0.755-0.899), a sensitivity of 0.767, a specificity of 0.733, a good discrimination and calibration, and a relatively high clinical net benefit.@*Conclusion@#The nomogram established by age, pre-pregnancy BMI, method of conception, PLGF and UtA-RI has a good predictive value for the risk of PE in twin-pregnant women.
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Objective To analyze the influencing factors of choroidopathy(choroidal atrophy and choroidal neovas-cularization)secondary to high myopia based on Logistic regression analysis and to construct a Nomogram risk prediction model based on the related factors,so as to provide guidance for clinical treatment.Methods A total of 340 patients(680 eyes)with high myopia admitted to Beijing Jishuitan Hospital from January 2021 to January 2023 were selected and di-vided into group A(170 patients,340 eyes)and group B(170 patients,340 eyes).The incidence of choroidopathy in the two groups was compared.The groups A and B were divided into two subgroups,subgroup a and subgroup b,according to whether choroidopathy occurred or not.Multivariate Logistic regression analysis was carried out to explore the influencing factors of choroidopathy secondary to high myopia.A Nomogram risk prediction model for choroidopathy secondary to high myopia was constructed based on the influencing factors and externally validated.Results In groups A and B,the age,proportion of diabetes mellitus,axial length,and level of seruim transforming growth factor β1(TGF-β1)of patients in subgroup a were higher than those in the subgroup b,and the diopter was lower than that in the subgroup b(all P＜0.05).The Logistic regression analysis showed that age,diabetes mellitus,axial length and serum TGF-β1 level were independent risk factors for choroidopathy secondary to high myopia,and diopter was a protective factor(all P＜0.05).Age,diabetes mellitus,axial length and serum TGF-β1 level were positively correlated risk factors for choroidopathy secondary to high myopia,and diopter was a negatively correlated risk factor(all P＜0.05).The area under the curve of the Nomogram risk prediction model for predicting choroidopathy secondary to high myopia was 0.818,and the calibration was good.Con-clusion Age,diabetes mellitus,axial length,diopter and serum TGF-β1 level are the influential factors for choroidopa-thy secondary to high myopia.The Nomogram risk prediction model established based on these factors has a certain value for predicting choroidopathy secondary to high myopia.The clinical therapeutic schedules should be made based on this model to reduce the risk of secondary choroidopathy.
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Objective:To analyze the risk factors of secondary epileptic seizures in children with febrile seizures and to construct a nomogram prediction model.Methods:A total of 235 children with febrile seizures who were admitted to Enshi State Hospital for Nationalities from August 2018 to September 2021 were selected. According to whether the children had secondary epileptic seizures during the 6-month follow-up, the children were divided into the seizure group (62 cases) and no-seizure group (173 cases). The best cut-off value of each factor were obtained by the receiver operating characteristic (ROC). Multivariate Cox regression analysis was used to analyze the independent risk factors of secondary seizures in children with febrile seizures. The R software "rms" package was constructed to predict secondary seizures in children with febrile seizures. High-risk nomogram models, calibration curves was used for internal validation of nomogram models, and decision curves to assess the predictive power of nomogram models.Results:The age of the patients in the seizure group was lower than that in the no-seizure group: (14.45 ± 1.54) months vs. (21.47 ± 2.18) months; and the proportion of family history of epilepsy, the proportion of perinatal (abnormal), the proportion of seizure type (comprehensive), the proportion of electroencephalogram (EEG) (abnormal), the number of seizures, the duration of seizure, the tumor necrosis factor-alpha (TNF-α) level in the seizure group were higher than those in the no-seizure group: 56.45%(35/62) vs. 35.84%(62/173), 59.68% (37/62) vs. 15.61%(27/173), 70.97%(44/62) vs. 36.99% (64/173), 74.19% (46/62) vs. 20.81% (36/173), (5.45 ± 2.32) times vs. (2.04 ± 1.02) times, (18.89 ± 4.29) min vs. (12.62 ± 2.34) min, (25.65 ± 5.32) ng/L vs.(18.21 ± 2.29) ng/L, there were statistical differences ( P<0.05). The area under the curve (ACU) of age, number of convulsions, duration of convulsion, and TNF-α were 0.906, 0.913, 0.899, and 0.890, respectively; the best cut-off values were 3 years, 4 times, 15 min, 21 ng/L; age (≤3 years), family history of epilepsy (yes), type of seizures (generalized), perinatal period (abnormal), number of seizures (≥4 times), duration of seizures (≥15 min) were febrile seizures independent risk factors for secondary epileptic seizures in children ( P<0.05), the C-index of this nomogram prediction model was 0.744 (0.567-0.932); the decision curve showed that when the risk threshold was greater than 0.11, the clinical net benefit provided by this prediction model. The benefits were all higher than individual independent risk factors and provided a significant additional net clinical benefit in predicting a high risk of seizures secondary to febrile seizures in children with febrile seizures. Conclusions:This study constructed a nomogram model of the risk of secondary seizures in children with febrile seizures based on age, family history of epilepsy, type of seizures, perinatal period, number of seizures, and duration of seizures. Important strategic guidance.
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Objective To analyze the factors influencing early-onset sepsis in preterm infants and construct nomogram model.Methods A total of 124 neonates with premature sepsis admitted to Shanxi Children's Hospital(Shanxi Maternal and Child Health Hos-pital)from January 2020 to December 2021 were collected.According to gestational age,the neonates were divided into premature group(n=33)and full-term group(n=91),and the clinical characteristics of the two groups were compared,and nomogram model was es-tablished to internally validate the predictiveness and accuracy of the model.Results Compared with the full-term group,the proportion of females in premature group was higher(x2=7.147,P＜0.05),the 1min Apgarscore in premature group was lower(x2=-3.398,P＜0.05),the proportion of perinatal mothers with pregnancy complications in premature group was higher(x2=7.846,P＜0.05),the incidence of pneumonia and poor response in preterm infants of premature group were higher(x2=18.210,P＜0.05;x2=14.814,P＜0.05),but the incidence of jaundice in premature group was lower(x2=10.400,P＜0.05).Multivariate Logistic regression analysis showed that female and pneumonia were risk factors for early-onset sepsis in preterm infants(P＜0.05).The results of the nomogram model showed that the C-index of the model was 0.886.The predicted incidence was generally consistent with the actual incidence,the area under the receiver operator characteristic curve was 0.886,and the decision curve showed a high net benefit value at threshold proba-bilities of 4％-100％.Conclusion Female,preterm infants with pneumonia have a higher risk of early-onset sepsis.The nomogram model of premature sepsis constructed in this study has high clinical value and can provide a reference basis for clinical prevention of early-onset sepsis in preterm infants.
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Objective To analyze the risk factors for hypothermia during modified radical mastectomy,and construct a nomogram model for predicting the occurrence of hypothermia during modified radical mastectomy based on the risk factors.Methods A total of 383 patients received modified radical mastectomy admitted to our hospital were selected and divided into the hypothermia group(n=58)and the normal group(n=325)according to whether hypothermia occurred.The clinical data of the patients were collected,and the univariate analysis and Logistic regression analysis were used to screen out the independent risk factors for intraoperative hypothermia,and a risk nomogram model for predicting intraoperative hypothermia was constructed by R software and verified.Results There were statistically significant differences in the hypothyroidism,preoperative basal body temperature,intraoperative room temperature,operation time,anesthesia time,intraoperative blood loss,and intraoperative infusion between the two groups(P<0.05).The hypothyroidism(OR=2.156,95%CI:1.158～4.016,P=0.015),abnormal preoperative basal body temperature(OR=2.451,95%CI:1.309～4.588,P=0.005),intraoperative room temperature<23℃(OR=2.027,95%CI:1.085～3.786,P=0.027),operation time>2 hours(OR=2.316,95%CI:1.239～4.327,P=0.008),anesthesia time>3 hours(OR=2.264,95%CI:1.206～4.252,P=0.011),intraoperative infusion volume>1 500 mL(OR=2.895,95%CI:1.543～5.432,P=0.001)were the independent risk factors for the occurrence of intraoperative hypothermia.The nomogram model showed that the score of intraoperative infusion volume>1 500 mL was 100 points,hypothyroidism was 93 points,anesthesia time>3 hours was 85 points,intraoperative room temperature<23℃was 84 points,operation time>2 hours was 79 points,and abnormal preoperative basal body temperature was 75 points.The nomogram model verification results demonstrated that the C-index was 0.834;the H-L goodness-of-fit test showed χ2=11.854 and P=0.078;the calibration curve was close to the ideal curve;the area under the receiver operating characteristic curve was 0.812;and the net benefit value was high at the threshold probability from 5%to 70%.Conclusion The nomogram model constructed in this study can more accurately and reliably predict the risk of hypothermia occurring during modified radical mastectomy,which meets the clinical need for an integrated model and helps to promote the steady development of individualized medicine.